The field of medical imaging has seen significant advances since the time X-Rays were first used to determine anatomical abnormalities. Medical imaging hardware has progressed in the form of newer machines such as Medical Resonance Imaging (MRI) scanners, Computed Axial Tomography (CAT) scanners, etc. Because of large amount of image data generated by such modern medical scanners, there has been and remains a need for developing image processing techniques that can automate some or all of the processes to determine the presence of anatomical abnormalities in scanned medical images.
Digital medical images are constructed using raw image data obtained from a scanner, for example, a CAT scanner, MRI, etc. Digital medical images are typically either a two-dimensional (“2-D”) image made of pixel elements or a three-dimensional (“3-D”) image made of volume elements (“voxels”). Four-dimensional (4-D) medical images containing information about 3-D volumes moving in time are also known. Such 2-D, 3-D or 4-D images are processed using medical image recognition techniques to determine the presence of anatomical structures such as lesions, cysts, tumors, polyps, etc. Given the amount of image data generated by any given image scan, however, it is preferable that an automatic technique should point out anatomical features in the to selected regions of an image to a doctor for further diagnosis of any disease or condition.
Automatic image processing and recognition of structures within a medical image is generally referred to as Computer-Aided Detection (CAD). A CAD system can process medical images and identify anatomical structures including possible abnormalities for further review. Such possible abnormalities are often called candidates and are considered to be generated by the CAD system based upon the medical images.
One particularly important use for medical imaging systems and CAD systems is in the review of lung images to detect and identify potentially malignant structures such as pulmonary nodules. Recent studies reveal that the location of the nodules in relation to other anatomical structures (e.g., fissure, pleural, vessel, etc.) provides contextual information that can help distinguish between benign and malignant lesions. For instance, vessel-attached pulmonary nodules are more likely to be cancerous than solitary (or purely intra-parenchymal) nodules, while fissure-attached or pleural-based (i.e. lung wall-attached) nodules are typically benign.
Despite its clinical importance, automatic classification of solitary pulmonary nodules and/or various attached nodules has not been extensively studied. Instead, manual labeling is typically performed. A trained radiologist physically inspects the images, and segments or colors the nodule and its local contextual structures on the voxel level. The connectivity of the nodule is manually labeled by determining whether the nodule touches the local contextual structures. This process is highly time-consuming, inefficient and prone to mistakes caused by fatigue and human error. Moreover, due to the low contrast and often ambiguous boundaries of the imaged structures, errors in analyzing the images are prevalent.
As such, there is a need for a robust framework that automatically or semi-automatically detects and classifies structures in image data.